Analysis And Integrated Modelling Of Biophysical And SocioEconomic Determinants of Cropping Systems in The Volta Delta

 ABSTRACT

Cropping systems productivity in the Volta Delta is dependent on complex interconnecting biophysical and socioeconomic determinants. The study analysed the determinants of cropping systems yield and developed an integrated model for managing desired sustainable crop production scenarios. The analysis was conducted on change in yield over time, rainfall, temperature, Land use/land cover (LULC) and soil characteristics. District level crop yield was regressed on the biophysical variables. Multinomial logistic regression was used to determine the socioeconomic factors that determine different household crop yield levels. Biophysical and socioeconomic determinants were integrated into the Bayesian Network (BN) model for yield predictions to support management and policy decisions. A statistically significant positive trend of the major wet season and the mean annual rainfall were observed for Ada and Akatsi while a statistically highly significant decreasing trend in rainfall was observed for Adidome. Minimum, maximum and mean temperatures showed increasing trends over time. The LULCC showed a 2.82% net gain for active croplands for the period 2002-2017 and a high net loss was experienced by grass savannah, mangrove, and shrub savannah and thicket: - 2.59%, -1.68% and -1.24%, respectively. The soils were mainly sandy, moderately acidic, and had low total nitrogen and potassium; however, the organic matter content and phosphorus were medium and high, respectively. Almost all the biophysical determinants significantly predicted crop yield. For the socioeconomic factors, male-headed households and households that ever-hired labour, were more likely to produce more than 200kg/acre of maise. Femaleheaded households that used fertiliser and farmed on a minimum of 6.01acres of land were more likely to produce at least 200kg/acre of vegetables. The BN model results showed that the likelihood of high cropping systems productivity is more sensitive to input use, averaged cropped area and soil health. Under the climate change scenario, a combination of high GDP, good government policies and improved soil organic matter estimated the likelihood of high cropping systems productivity at 61.2%. Government, relevant institutions and agencies should strengthen the adaptive capacity of farmers by providing an accurate forecast of rainfall information, small-scale irrigation and encourage adoption of drought-resistant crop varieties and crop diversification. This will minimise the effects of the observed rainfall and temperature trends and variabilities in the Delta. The study also recommends the development of a dynamic version of the BN model and improving empirical data generation for enhanced model forecasting capacity, widening of its applicability and performance.